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Special Issue Information

Dear Colleagues,

In recent years, we have seen a proliferation in the provision and use of satellite soil moisture data derived from active and passive microwave sensors (SMAP, SMOS, ASCAT, AMRS-2, Sentinel-1, etc.) and optical/thermal imagers. However, there are still many open scientific question related to the way of how soil moisture data are being retrieved, validated and applied. Additionally, there is a high need to develop novel approaches for improving the spatio-temporal sampling of the data and their accuracy. Therefore, the purpose of this Special Issue is to discuss and reconcile recent methodological advances in the development, validation and application of global satellite soil moisture data. This Special Issue is related to the 4th Satellite Soil Moisture Validation and Application Workshop, which will place at the Vienna University of Technology (TU Wien) from 19–20 September 2017 (http://smw.geo.tuwien.ac.at/). Topics to be discussed at the workshop are:

What is the quality of the current satellite products and what can we expect in the near future?

What is information content at Level 1 and how to exploit the availability of multiple satellites?

Who is using satellite soil moisture data and for what purpose?

What are the best practices in validating soil moisture products?

What are the main limitations of satellite soil moisture data from a user’s perspective?

What is the future of satellite-based soil moisture remote sensing?

We invite in particular authors attending the workshop to submit their papers to this Special Issue of Remote Sensing.

Authors are required to check and follow the specific Instructions to Authors, http://www.mdpi.com/journal/remotesensing/instructions.

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar

In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar Scatterometer (ASCAT), embarked on the Meteorological Operational satellites (MetOp). The product generated with these measurements has a horizontal resolution of 25 km and represents the upper few centimeters of soil. Our approach is based on the Ensemble Kalman Filter technique (EnKF), where observation and model uncertainties are taken into account, implemented in a conceptual hydrological model. The analysis is carried out in the Demer catchment of the Scheldt River Basin in Belgium, for the period from June 2013–May 2016. In this context, two methodological advances are being proposed. First, the generation of stochastic terms, necessary for the EnKF, of bounded variables like SSM is addressed with the aid of specially-designed probability distributions, so that the bounds are never exceeded. Second, bias due to the assimilation procedure itself is removed using a post-processing technique. Subsequently, the impact of SSM assimilation on the simulated streamflow is estimated using a series of statistical measures based on the ensemble average. The differences from the control simulation are then assessed using a two-dimensional bootstrap sampling on the ensemble generated by the assimilation procedure. Our analysis shows that data assimilation combined with bias correction can improve the streamflow estimations or, at a minimum, produce results statistically indistinguishable from the control run of the hydrological model.
Full article

The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the

The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ°) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson’s correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m3/m3 for the ascending data, and are 0.012, 0.026, and 0.055 m3/m3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites.
Full article

A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input

A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias ≤ 0.02) and root mean square error values (RMSE ≤ 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China.
Full article